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A comparison of art style transfer in Cycle-GAN based on different generators J. Phys. Conf. Ser. Pub Date : 2024-02-01 Xu Ma
With the rapid development of deep neural networks in computer vision, style transfer technology has also made significant progress. Cycle-GAN can perform object deformation, style transfer, and image enhancement without one-to-one mapping between source and target domains. In the painting style transfer task, the performance of Cycle-GAN is recognized. In Cycle-GAN, the choice of generator model is
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Research on laser-based mobile robot SLAM and autonomous navigation J. Phys. Conf. Ser. Pub Date : 2024-02-01 Zhihong Huang, Jianping Geng
SLAM (Simultaneous Localization and Mapping) and autonomous navigation systems, as fundamental components of mobile robots, largely determine their ability to accomplish tasks. While research on laser-based SLAM and autonomous navigation algorithms in simulated environments has achieved significant success, there is limited research on deploying these algorithms on physical mobile robots, conducting
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Leaf disease detection using deep Convolutional Neural Networks J. Phys. Conf. Ser. Pub Date : 2024-02-01 Mingyu Hu, Shanru Long, Chenle Wang, Ziqi Wang
The automatic recognition of plant diseases is of crucial importance for the current development of agriculture. Fast and efficient identification can greatly reduce the natural, economic, and human resource loss caused to agricultural practitioners. Deep neural networks allow computers to learn plant disease detection in an end-to-end manner, thereby obtaining better results and higher efficiency
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High precision map crowdsource update technology and SLAM technology - Application in autonomous driving J. Phys. Conf. Ser. Pub Date : 2024-02-01 Dapeng Liu
In response to the challenges faced by real-time dynamic updates of high-precision maps for autonomous driving in terms of high precision, high reliability, and high safety, this paper summarizes the difficulties and challenges currently faced by high-precision map updates. Accelerate the large-scale commercialization of autonomous driving high-precision maps. Thus, improving the safety and stability
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Knee replacement patients and wearable knee pads J. Phys. Conf. Ser. Pub Date : 2024-02-01 Yishen Zhang
This paper proposes a novel solution to the common problem of knee stiffness experienced by patients following knee replacement surgery. The paper suggests designing a wearable knee pad that is fitted with three 6-axis IMU sensors to monitor, evaluate, and process the movement data of the patient’s knee in real-time. The data collected would then be used to provide appropriate recovery methods and
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Highly immersive imaging: Depth of field effect implemented through ray tracing with multiple samples J. Phys. Conf. Ser. Pub Date : 2024-02-01 Zhenkai Zhong
This paper explores the implementation of depth of field effects through ray tracing with multiple samples. We focus on simulating the imaging process of the human eye, giving more consideration to the factors influencing the depth of field effect. Building upon ray tracing shading computations, we have employed a multi-sampling technique for rendering. This approach complements spatial information
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A Comparative Study on Deep Networks for Glaucoma Classification J. Phys. Conf. Ser. Pub Date : 2024-02-01 Zifan Ying, Zhichong Wang, Hongbo Zhang, Rongxuan Zhang
The purpose of this study is to classify glaucoma and non-glaucoma images from REFUGE dataset of fundus images. Due to the imbalance of dataset, we did data augmentation and preprocessing for dataset first (including feature extraction and enhancement). We then tested the performance of some deep convolutional neural networks as baselines, including ResNet, GoogLeNet, and VGGNet. Later we introduced
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An improved DDPG algorithm based on evolution-guided transfer in reinforcement learning J. Phys. Conf. Ser. Pub Date : 2024-02-01 Xueqian Bai, Haonian Wang
Deep Reinforcement Learning (DRL) algorithms help agents take actions automatically in sophisticated control tasks. However, it is challenged by sparse reward and long training time for exploration in the application of Deep Neural Network (DNN). Evolutionary Algorithms (EAs), a set of black box optimization techniques, are well applied to single agent real-world problems, not troubled by temporal
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Comparative analysis of various models for image classification on Cifar-100 dataset J. Phys. Conf. Ser. Pub Date : 2024-02-01 YuYu Zheng, HaoXuan Huang, JunMing Chen
Nowadays, people developed various convolutional neural network (CNN) based models for computer vision. Some famous models, such as GoogLeNet, Residual Network (ResNet), Visual Geometry Group (VGG), and You Only Look Once (YOLO), have different architecture and performances. Determining which model to use may be a troublesome problem for those just starting to study image classification. To solve this
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The parasitic capacitance considerations of metal interconnects in sub 10 nm era J. Phys. Conf. Ser. Pub Date : 2024-02-01 Qizhe Wu
With the development of the integrated circuit industry and semiconductor technology, we have entered the sub-10 nanometers era, which means the distance between adjacent components in a device is less than 10 nm. This is a situation where the parasitic capacitance of metal interconnects must be considered. Parasitic capacitance can act as a significant influence in different ways on disparate devices
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Decentralized Face Identification with Hierarchical Navigable Small World on Blockchain J. Phys. Conf. Ser. Pub Date : 2024-02-01 Hsiang-Hung Lee, Yiting Chen
This paper presents a novel method for decentralized storage in deep-learning-based face recognition systems using the Hierarchical Navigable Small World (HNSW) algorithm. The proposed solution utilizes Ethereum smart contracts, which acts as highly available data storage systems for storing identifiable data for authorized personnel. In addition, the solution is integrated with a centralized vector
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A design of a household output water temperature control system based on the PID controller and feedforward control J. Phys. Conf. Ser. Pub Date : 2024-02-01 Hainan Liu
Household bathroom products are facing the problems of input water temperature fluctuation and effluent temperature control delay, which brings trouble to consumers. This essay will propose a solution that controls the water temperature at the effluent end by adding a water tank and an electrical heater. The author establishes a mathematical model of the water tank and calculates the transfer functions
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Energy consumption forecasting with deep learning J. Phys. Conf. Ser. Pub Date : 2024-02-01 Yunfan Li
This research endeavors to create an advanced machine learning model designed for the prediction of household electricity consumption. It leverages a multidimensional time-series dataset encompassing energy consumption profiles, customer characteristics, and meteorological information. A comprehensive exploration of diverse deep learning architectures is conducted, encompassing variations of recurrent
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Improving polygon image segmentation by enhancing U-Net architecture J. Phys. Conf. Ser. Pub Date : 2024-02-01 Da Li
The crucial task of polyp recognition in medical imaging plays a pivotal role in the early detection and prevention of colorectal cancer. Semantic segmentation, particularly utilizing sophisticated deep learning models such as U-Net, has demonstrated promising results in the realm of polyp segmentation. However, the traditional U-Net structure sometimes grapples with accurately delineating the edges
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Explainer on GNN-based segmentation networks J. Phys. Conf. Ser. Pub Date : 2024-02-01 Shuaimin Wu
Graph Neural Networks (GNN) are powerful tools for deep learning. Similar to other neural networks, GNNs are complex models, in which humans can’t understand the decision-making procedures of the models. Therefore, it brings the need to explainability of GNNs. Explainability is critical for deep learning to support its predictions. In this paper, we will investigate the Grad-Cam and Integrated-Gradients
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Asymptotic feature pyramid based YOLOv5s for birds detection J. Phys. Conf. Ser. Pub Date : 2024-02-01 Jiajie Cai, Han Huang, Feiyang Song
The detection of all sorts of birds has become increasingly important in the fields of ecological balance and biological protection. To tackle the problems of low accuracy, high omission rate and low detection confidence levels in the application of artificial intelligence and deep learning in bird detection, this paper proposes a bird detection method that leverages the YOLOv5s model and incorporates
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Improving small sample medical image segmentation using CBAM: Insights from two datasets J. Phys. Conf. Ser. Pub Date : 2024-02-01 Yusheng Tan
Medical image segmentation is one of the key tasks in the medical field and is crucial for accurate lesion detection and treatment planning. However, the small sample problem has been one of the challenges in medical image segmentation. In this study, the small sample medical image segmentation problem is evaluated experimentally based on two different datasets, the VOC dataset and the esophageal medical
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Review: Recent advances for the diffusion model J. Phys. Conf. Ser. Pub Date : 2024-02-01 Yufeng Wei
As the generative model technology becomes more and more popular, more and more people have invested in the research of the current State-of-the-art (SOTA) generative model-diffusion model. This paper reviews all SOTA generation models using the diffusion model for text-to-image generation since the emergence of the diffusion model, including the denoising diffusion probabilistic model (DDPM), DALL·E
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Graph sampling based deep metric learning for cross-view geo-localization J. Phys. Conf. Ser. Pub Date : 2024-02-01 Haozhang Jia
Cross-view geo-localization has emerged as a novel computer vision task that has garnered increasing attention. This is primarily attributed to its practical significance in the domains of drone navigation and drone-view localization. Moreover, the work is particularly demanding due to its inherent requirement for cross-domain matching. There are generally two ways to train a neural network to match
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Comparison and analysis of accuracy of various machine learning algorithms in abnormal state monitoring of internet of things devices J. Phys. Conf. Ser. Pub Date : 2024-02-01 Bolun Zhang
With the development of Internet of Things technology, more and more devices are connected to the Internet, including not only traditional computers, mobile phones and other smart terminal devices, but also various sensor devices. These sensor devices can collect a variety of environmental information and physical quantities, such as temperature, humidity, air pressure, light intensity, vibration,
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Research on unmanned navigation and PID control method for aircraft tractor J. Phys. Conf. Ser. Pub Date : 2024-02-01 Juntian Liu
Unmanned navigation and control systems are gaining traction in various industries, including aviation. The related issues of control systems are also hot topics in research. This paper comprehensively studies the application of unmanned navigation and the implementation of proportional integral differential (PID) control methods for aircraft tractors. Aiming at the characteristics of large deviation
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Attention-based deep learning approach for CSI feedback under 5G TDL channel J. Phys. Conf. Ser. Pub Date : 2024-02-01 Hanli Peng
In 5G communication systems, accurate channel state information (CSI) is indispensable for signal detection and regulation at the base station side. However, frequent CSI feedback from users leads to excessive system overhead. To tackle this challenge, this paper puts forward a novel deep learning framework - HCNet based on attention mechanism and autoencoder, aiming to efficiently compress and reconstruct
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Preface J. Phys. Conf. Ser. Pub Date : 2024-02-01
The International Conference on Machine Learning and Automation (CONF-MLA) is an annual conference focusing on research areas including the development of engineering and machine learning applications. It aims to establish a broad and interdisciplinary platform for experts, researchers, and students worldwide to present, exchange, and discuss the latest advances and developments in relevant fields
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Peer Review Statement J. Phys. Conf. Ser. Pub Date : 2024-02-01
All papers published in this volume have been reviewed through processes administered by the Editors. Reviews were conducted by expert referees to the professional and scientific standards expected of a proceedings journal published by IOP Publishing.1. Type of peer review: Single anonymous2. Conference submission management system: Morressier3. Number of submissions received: 1784. Number of submissions
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Detailed Analysis of Energy Consumption for an Office Building J. Phys. Conf. Ser. Pub Date : 2024-02-01 N Bazenkov, I Petrov
Modern buildings consume a significant share of global power production. The primary sources of the power consumption are heating, ventilation and air conditioning (HVAC), light, cooking equipment, technical equipment like elevators, office facilities. Optimization of the buildings power consumption requires real-life data both for strategic planing and operational control. Here we analyze detailed
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Dynamic processes in the liquid crystal-water emulsion under shear flow and electric field J. Phys. Conf. Ser. Pub Date : 2024-02-01 Anastasiia Vasileva, Sergey Pasechnik
We present the results of experimental investigation of dynamical processes arising in liquid crystal-water emulsion under action of a decay shear flow and ac electric field. The 10 : 1 emulsion, consisting of the well-studied nematic mixture E7 and ionized water was prepared by usage of ultrasonic (29 kHz) mixer. It made possible to get a number of water droplets with the distribution of diameters
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3D Texture Segmentation using Supervised Methods J. Phys. Conf. Ser. Pub Date : 2024-02-01 Zainab Ali Adan, Mohamed Soufiane Jouini, Fawaz Hjouj
Supervised learning methods have been widely used for image classification in various fields, including medical and industrial sectors. Some of these methods are traditional and possess certain limitations when addressing complex problems. The most common and effective approaches involve Convolutional Neural Networks (CNNs), such as U-Net. However, most studies employ CNNs in their 2D structures, which
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Quasi-3D Numerical Thermal Modelling of Electronic Systems in Package J. Phys. Conf. Ser. Pub Date : 2024-02-01 Konstantin O. Petrosyants, Nikita I. Ryabov
The technology of three-dimensional (3D) packaging currently provides an increase in the efficiency of various electronic systems, bringing the volume of the package and heat dissipation into proper correlation. For many types of modern 3D packages the electro-thermal properties were not yet investigated. So the thermal modelling of 3D system in package (SiP) constructions became obliged for package
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Digital Twin of the Photoelectric Converter of the Power Transmission System over Optical Fiber J. Phys. Conf. Ser. Pub Date : 2024-02-01 A. A. Garkushin, V. V. Krishtop, S. A. Storozhev, I. L. Volkhin, E. V. Nifontova, E. V. Urbanovich, D.A. Kustov, I. V. Kadochikov
The photovoltaic converter (PVC) is a key device in a power-over-fiber system (PoFS). The aim of the work is to create a digital twin (DT) to predict the behaviour of PVC based on their specification data, which is the first step towards creating a universal DT of the entire PoFS. The paper considers the theoretical principles of operation of PVC using the zone theory of solids. A relationship has
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Data Driven PMV-Comfort and Energy Consumption Control in Common Buildings J. Phys. Conf. Ser. Pub Date : 2024-02-01 Yury Rassadin, Nikita Shushko
HVAC systems are essential in the energy management of commercial buildings. The main goal for HVAC system is to improve productivity of the inhabitants by providing comfortable indoor environment. The most common tool for evaluating comfort is PMV index, non-linear combination of indoor environment state variables. The Paper considers optimization of energy consumption of the building by combining
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Technological ways to improve the efficiency of petrothermal power plants and increase their contribution to electricity generation J. Phys. Conf. Ser. Pub Date : 2024-02-01 A F Pashchenko
The contribution of renewable energy sources to the total electricity production in the world is steadily increasing. At the same time, the share of geothermal energy in the overall balance remains unjustifiably insufficient. The prospects for obtaining heat from the earth are limited by a number of technical and technological difficulties, which especially applies to that part of geothermal energy
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Unsupervised texture classification of 3D X-ray Micro-computed Tomography images J. Phys. Conf. Ser. Pub Date : 2024-02-01 Tamara A. I. Almeghari, Mohamed Soufiane Jouini, Fawaz Hjouj
Characterizing rock proprieties is crucial in the oilfield to evaluate hydrocarbon reserves. Several studies showed a high correlation between rock properties and textures. Therefore, we propose integrating texture information in the images to identify precisely the most representative textures in highly heterogeneous rocks to estimate their properties. First, we implemented a steerable pyramid decomposition
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Identification of Variables Affecting Levels of Salt Concentrations in Shatt Al-Arab Water Using Modified Kernel Principal Component Analysis J. Phys. Conf. Ser. Pub Date : 2024-02-01 Ahmed Husham Mohammed Albasri, Marwan Abdul Hameed Ashour
This study paper is an attempt to bring to light a new approach in the treatment of the Gaussian function. The Gaussian function is considered the basis for building the elements of the kernel matrix within the methodology of the kernel principal components that aims to reduce the dimensions and then determine the most influential variables. Besides, it works on reducing the mathematical complexity
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Simulation of Active Power Generation During Operation of a Steam Turbine Stage in Low-Steam and Motor Modes J. Phys. Conf. Ser. Pub Date : 2024-02-01 E K Arakelyan, A V Andryushin, F F Pashchenko, S V Mezin, K A Andryushin, A A Kosoy
The operation mode of the steam turbine stage in low-steam and motor modes is considered. The features of the operation of the steam turbine stage in the motor mode are given. It is shown that the condition accepted in earlier studies of hydrodynamic processes in the turbine stage during its operation in the motor mode, that the cooling steam flows through the stage without expansion, i.e. without
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Modeling of the beam core in phase space using kernel density estimation J. Phys. Conf. Ser. Pub Date : 2024-02-01 Yasuaki Haba
Phase space is a mathematical construct that encompasses particle positions and their corresponding momenta. In general, discrete phase space structures are experimentally measured due to the limited spatial and angular resolutions of individual devices. From the perspective of beam focusing characteristics, beams are discussed in terms of core components and halo components. While the beam core is
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Optimized PID and NN-based Speed Control of a Load-coupled DC Motor J. Phys. Conf. Ser. Pub Date : 2024-02-01 Ángel Encalada-Dávila, Kareim Mohamed Ellithy, Mariam Salah AbdElhalim, Raafat Shalaby
In this work, three control strategies are presented, compared, and discussed, applied on a load-coupled DC motor. The purpose is to control in an optimal way the motor speed in terms of the armature voltage. Two strategies are based on PID control, working on the classical PID controller and the optimized one by using particle swarm optimization (PSO) to tune the PID controller parameters. The other
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Modeling of finned flat tube heat exchangers and search of Nusselt-Reynolds numbers correlations J. Phys. Conf. Ser. Pub Date : 2024-02-01 A Grekova, A Lysikov, M Solovyeva, M Tokarev
Using a wind tunnel, a series of model finned flat tube copper radiators was studied. All geometrical parameters of the studied radiators were fixed except for the height of the fin, which was varied in the range from 5 to 20 mm. It was found that the global water-air heat transfer coefficient weakly depends on the water flow rate and significantly depends on the air flow rate. Correlations between
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Inverse problem for retrieving greenhouse gas fluxes at the non-uniform underlying surface from measurements of their concentrations at several levels J. Phys. Conf. Ser. Pub Date : 2024-02-01 I V Mukhartova, A V Olchev, R R Gibadullin, D V Lukyanenko, L Sh Makmudova, I A Kerimov
The study focuses on the formulation, analysis, and solution of the remote sensing inverse problem to retrieve surface carbon dioxide (CO2) fluxes from measurements of CO2 concentrations at different levels within the atmospheric boundary layer. A three-dimensional hydrodynamic model of turbulent greenhouse gas (GHG) transport was used as a forward model to link the surface GHG fluxes to the drone
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Recognition of differential culture conditions with dimensional reduction approach J. Phys. Conf. Ser. Pub Date : 2024-02-01 Koji Ishiya, Takeaki Taniguchi
Microorganisms constantly modify their gene expression and metabolic profiles in response to alterations in their surrounding environment. Monitoring these changes is crucial for regulating microbial production of substances. However, it remains challenging to identify differential culture conditions through the extraction of differentially expressed genes and clustering of gene expression profiles
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Quantum and quantum-inspired optimization for solving the minimum bin packing problem J. Phys. Conf. Ser. Pub Date : 2024-02-01 A A Bozhedarov, S R Usmanov, G V Salakhov, A S Boev, E O Kiktenko, A K Fedorov
Quantum computing devices are believed to be powerful in solving hard computational tasks, in particular, combinatorial optimization problems. In the present work, we consider a particular type of the minimum bin packing problem, which can be used for solving the problem of filling spent nuclear fuel in deep-repository canisters that is relevant for atomic energy industry. We first redefine the aforementioned
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Numerical solution of the Schrödinger equation using Neural Networks in Python J. Phys. Conf. Ser. Pub Date : 2024-02-01 A. Gkrepis, O. Kosmas, D. Vlachos, T. Kosmas
The motion of quantum mechanical systems in physical sciences is described by partial differential equations, usually of second order with respect to spatial coordinates. The required solutions of the time-dependent type of equations are, in general, functions of the temporal variable t and the state vectors determining the positions of the system’s particles at the time t. Only a small number, however
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Assessment of thermal comfort in the modern lecture theater: Kielce University of Technology, Poland - case study J. Phys. Conf. Ser. Pub Date : 2024-02-01 N Krawczyk, L Dębska, H Alzaben
This paper investigates student’s thermal comfort in the intelligent building called “Energis” of Kielce University of Technology located in Poland, which is equipped with advanced heating, ventilation and air conditioning (HVAC) mechanical systems. One lecture theater is selected for thermal analysis. Analysis was focused on student’s assessment of thermal sensations in the autumn season to determine
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Comparison of Computational Methods and Approaches Applied in Formulation of Boundary Conditions in Lagrange’s Ballistic Problem J. Phys. Conf. Ser. Pub Date : 2024-02-01 D M Cichy, B Fikus, R K Trębiński
This paper compares algorithms for constructing a differential solution to the gas dynamics equations in the surrounding of a moving boundary. In order to compare the algorithms, the Lagrange problem, also known as the piston problem, was chosen as a test problem. A comparison was made between the author’s algorithm based on the method of characteristics and the classical approach with a fictitious
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Calculation of Deflagration Appearence in Hydrogen-Air Mixtures Flows in Regions with Obstacles J. Phys. Conf. Ser. Pub Date : 2024-02-01 S N Martyushov
Many researches devoted to deflagration appearance in hydrogen-air mixes and transition it to detonation. Preference of hydrogen as a fuel is detonation fuel cycle which is more energetic preferable in comparing with ordinary fuel cycle. In connection with this preference problem of hydrogen detonation engine constructing is extreme actual. Essential part of investigations in this field is developing
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Comparative reliability analysis of electric aircraft versions for NASA’s X-57 based on Lz-transform method J. Phys. Conf. Ser. Pub Date : 2024-02-01 S Gejo, J Kammermann, I Bolvashenkov, I Frenkel, Hans-Georg Herzog
As the goals of air transport shift towards more-electric or all-electric airplanes, different drive train configurations have been explored recently. A major goal on the way towards the inclusion in commercial air traffic is high reliability. One of the experimental electric airplanes is NASA’s X-57 “Maxwell”, which consists of fourteen electric motors powered from a battery pack. The aim of this
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Quantum solution of the relationship between the 19-vertex model and the Jones polynomial J. Phys. Conf. Ser. Pub Date : 2024-02-01 T K Kassenova
The challenge is to create an efficient quantum algorithm for the bosonic model capable of calculating the Jones polynomials for a knot resulting from interweaving or interlacing n-vertices. This weave is the construction of braid group representations from nineteen-vertex model. We present eigenbases and eigenvalues for lattice generators and their usefulness for the direct computation of Jones polynomials
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Procedure for Determining the Upper Bound on Absolute Error for LVDT Sensors J. Phys. Conf. Ser. Pub Date : 2024-02-01 Lucyna Szul, Krzysztof Tomczyk
This paper presents the procedure for determining the upper bound on absolute error (UBAE) for linear variable differential transformer (LVDT) sensors without inter-winding capacitance. This procedure is based on the results of a parametric identification of the sensor, from which the coefficient values of the corresponding transfer function are obtained. The UBAE is then determined by a simulation
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Transformers in High-Frequency Trading J. Phys. Conf. Ser. Pub Date : 2024-02-01 Konstantinos T. Kantoutsis, Adamantia N. Mavrogianni, Nikolaos P. Theodorakatos
Transformer is a deep learning model that, having an innovative performance in many tasks, has uniquely and significantly modified all the cast of mind of the AI scientific community. In this paper, we introduce a Transformer model that is applied to 1-minute timescale in the EURUSD and GBPUSD instruments of forex trading. We use the classical Transformer architecture without the Decoder since the
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Invasive-Invaded Interaction Incorporating a Bramson Model with Density-Dependent Diffusion and a Non-Lipschitz Reaction J. Phys. Conf. Ser. Pub Date : 2024-02-01 José Luis Díaz Palencia
The primary objective of the presented study is to investigate the pairwise interaction dynamics between invasive and invaded species, considering a model characterized by a non-regular, non-Lipschitz type reaction, as well as non-homogeneous diffusion. To achieve this, we begin with the foundational model proposed by Bramson in 1988 and tailor it to account for density-dependent diffusion and the
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Development and Validation of a 3-D Analytical Fluidic Model in Cartesian Coordinates for a Magnetic Refrigeration Application J. Phys. Conf. Ser. Pub Date : 2024-02-01 Julien Eustache, Antony Plait, Frédéric Dubas, Raynal Glises
This paper focuses on the development and validation of a three-dimensional (3-D) analytical model based on the formal resolution of the incompressible Navier-Stokes equations in Cartesian coordinates. This analytical fluidic model calculates the evolution of the fluid flow velocity for various pressure gradient shapes (i.e., square, trapezoidal, ramp, triangle, sinusoidal signals, etc.), with or without
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Life as the Explanation of the Measurement Problem J. Phys. Conf. Ser. Pub Date : 2024-02-01 Szymon Łukaszyk
This study argues that a biological cell, a dissipative structure, is the smallest agent capable of processing quantum information through its triangulated, holographic sphere of perception, where this mechanism has been extended by natural evolution to endo and exosemiosis in multicellular organisms and further to the language of Homo sapiens. Thus, life explains the measurement problem of quantum
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Boltzmann Equation and Knudsen Group - Boundary Shape and Boundary Conditions J. Phys. Conf. Ser. Pub Date : 2024-02-01 Jörg-Uwe Löbus
A Boltzmann type equation is considered where both, the physical and the velocity space, are bounded. It is assumed that the boundary conditions consist of a reflective as well as a diffusive component. Existence of positive bounds from below and above has been proved. It has been demonstrated that under conditions on the shape of the boundary, the underlying Knudsen type transport semigroup can be
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Learning systems of ordinary differential equations with Physics-Informed Neural Networks: the case study of enzyme kinetics J. Phys. Conf. Ser. Pub Date : 2024-02-01 Paola Lecca
Physics Informed Neural Networks (PINNs) are a type of function approximators that use both data-driven supervised neural networks to learn the model of the dynamics of a physical system, and mathematical equations of the physical laws governing that system. PINNs have the benefit of being data-driven to train a model, but also of being able to assure consistency with the physics, and to extrapolate
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Planckian effects on Ising model in an external electric field J. Phys. Conf. Ser. Pub Date : 2024-02-01 Fidele J. Twagirayezu
In this article, we study Planckian effects on statistical thermodynamic properties of one dimensional polarized Ising model with an external electric field. We introduce Planckian effects by rewriting the Lagrangian of a static electromagnetic field in terms of deformed operators to obtain the corrected Lagrangian for a static electric field, and then deduce the corrected static electric field. Then
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On the relationship between the intensity of zero-point fluctuations of an electromagnetic field (ZPFs) and the magnitude of an elementary electric charge J. Phys. Conf. Ser. Pub Date : 2024-02-01 Vladimir V Koltsov
The consequences of the idea of stimulation of spontaneous emission of photons and scattering of particles in quantum electrodynamics by zero-point fluctuations of an electromagnetic field (ZPFs) are considered. It is shown that this idea leads to a connection between the magnitude of the elementary electric charge and the intensity of the ZPFs, leads to the idea of the stochastic nature of the electromagnetic
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Numerical simulation of water jet load induced by spherical bubble collapse under underwater explosion J. Phys. Conf. Ser. Pub Date : 2024-02-01 J Yu, X P Zhang, Z X Sheng, Y Hao, C Shen, J P Chen, J Zhang
In this study, a numerical method combining the HLLC solver and FV-WENO scheme is proposed to accurately simulate the behavior of multiphase compressible fluids and investigate the formation process of water jets. The HLLC solver approximates the conservation equations of the fluid by using numerical fluxes from the approximate Riemann problem, while the FV-WENO scheme improves the accuracy of the
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Semigroup theory and finite element method applied to a non-linear dissipative wave equation J. Phys. Conf. Ser. Pub Date : 2024-02-01 Gino Chávez, Luis Cortés-Vega, Adrián Sotomayor
We study the wave equation with a non-linear dissipative term associated to a bidimensional membrane with fixed boundary. We use the semigroup theory to consider the existence and uniqueness of solutions to the problem and we implement the finite element method to analyse the vibrating evolutionary equation. In particular we use Comsol Multiphysics software with a rectangular mesh to analyze the corresponding
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Rough Surface Aerodynamic Computation in Rarefied Gas Flow Applying the Solution of Inverse Problem J. Phys. Conf. Ser. Pub Date : 2024-02-01 Iskander Khalidov, Olga Aksenova
Most effective method to find the roughness parameters in rarefied gas flow is to calculate them from aerodynamic measurements, solving the inverse problem. The value of the main roughness parameter obtained from the solution of inverse problem is substantially higher (at least 1,25–1,5 times) than similar value of the same parameter measured from the profile diagrams. Thus, the effect of surface roughness
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CFD simulation of double-pass solar dryer with air return J. Phys. Conf. Ser. Pub Date : 2024-02-01 Y N Droguy, P E Koffi, A Sissoko, J Andji, Y Soro, P Gbaha
An indirect solar dryer was designed and manufactured in Yamoussoukro, Côte d’Ivoire. It is equipped with three parameters including two air intake ports, a fan and a removable hot air recovery system. Vacuum CFD simulations were performed in order to observe the influence of these parameters on the effectiveness of the device. The fan switched off speeds of 9 m/s and made the air distribution uniform
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Application of higher-order FV-WENO scheme to the interaction between shock wave and bubble J. Phys. Conf. Ser. Pub Date : 2024-02-01 J Yu, Y Hao, Z X Sheng, X P Zhang, J P Chen, J Zhang, J Yang
The high-order finite volume-WENO (Weighted Essentially Non-Oscillatory) scheme combines the finite volume method with the WENO method, allowing for high-order accuracy and accurate simulation of complex physical phenomena. It has advantages in handling shock waves and bubble interactions. The idea of this method is to discretize the physical equations into a set of conservation equations and use the